Temporal techniques in remote sensing of global vegetation

Abstract

Time series processing is an important ingredient of a biophysical algorithm in order to get the expected continuous and smooth dynamics required by many applications. Several temporal techniques have been proposed to reduce noise and fill gaps in the time series of satellite data. The choice of the compositing method may have a large impact on the accuracy of the phenology extracted from the reconstructed time series. This chapter presents a comparison of six methods to improve the temporal coherence and continuity of leaf area index (LAI) time series. The temporal smoothing gap filling (TSGF) method which is based on an adaptive Savitzky-Golay filter combined with a linear interpolation approach for filling gaps over a limited temporal window showed the best performance when applied to time series with less than 60% of gaps. A climatology based approach outperformed other approaches for filling gaps in time series with more than 60% of missing data or when the period of missing data is longer than 100 days. Based on these findings, a dedicated approach combining the local TSGF filter with a climatology gap filling technique was developed. It constitutes the basis of the algorithm for the operational production of continuous and smooth time series of biophysical variables from VEGETATION data within the European Copernicus Global Land Service.